import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
fig, ax = plt.subplots()
ax.plot('date', 'GOOG', data=stocks)
# set title
ax.set_title('Google stock')
# horizontal axis
ax.set_xlabel('date')
# vertical axis
ax.set_ylabel('stock value')
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
# YOUR CODE HERE
fig, ax = plt.subplots()
ax.plot('date', 'GOOG', data=stocks)
ax.plot('date', 'AAPL', data=stocks)
ax.plot('date', 'AMZN', data=stocks)
ax.plot('date', 'FB', data=stocks)
ax.plot('date', 'NFLX', data=stocks)
ax.plot('date', 'MSFT', data=stocks)
# set title
ax.set_title('Stock Data')
# horizontal axis
ax.set_xlabel('date')
# vertical axis
ax.set_ylabel('stock value')
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
# show legends
plt.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
QUESTION: "Does smoker gives more tips than non-smoker?"
# YOUR CODE HERE
g = sns.FacetGrid(tips, col='smoker', hue='sex')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.show()
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE
import plotly.graph_objects as go
df = px.data.stocks()
list = df.columns
fig = px.line(df, x="date", y=list, markers='True')
fig.update_traces(marker_symbol='square')
fig.show()
# YOUR CODE HERE
fig = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="smoker")
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new
| year | lifeExp | pop | gdpPercap | iso_num | |
|---|---|---|---|---|---|
| continent | |||||
| Africa | 104364 | 2849.914 | 929539692 | 160629.695446 | 23859 |
| Americas | 50175 | 1840.203 | 898871184 | 275075.790634 | 9843 |
| Asia | 66231 | 2334.040 | 3811953827 | 411609.886714 | 13354 |
| Europe | 60210 | 2329.458 | 586098529 | 751634.449078 | 12829 |
| Oceania | 4014 | 161.439 | 24549947 | 59620.376550 | 590 |
# YOUR CODE HERE
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, color=df_2007_new.index, orientation='h', text_auto='.2s')
fig.update_traces(textposition='outside', textfont_size=14)
fig.update_yaxes(categoryorder="max ascending")
fig.update_layout(showlegend=False)
fig.show()